ed compared to model-based iterative reconstruction. https://www.selleckchem.com/products/jke-1674.html ? Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.
? Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. ? Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. ? Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.To investigate the usefulness of neurite orientation dispersion and density imaging (NODDI) in evaluating cortical tubers, especially epileptogenic tubers in tuberous sclerosis complex (TSC) patients.
High-resolution conventional MRI and multi-shell diffusion-weighted imaging were performed in 27 TSC patients. Diffusion images were fitted to NODDI and DTI models. Tubers were visually assessed on different image types and scored by two neuroradiologists. For 10 patients who underwent epilepsy surgery, the contrast ratios between lesion and background tissue were measured on different image types, and these were compared between 16 epileptogenic tubers and 92 non-epileptogenic tubers.
There were significant differences in lesion conspicuity scores and lesion-background contrast ratios across different sequences (both p&lt;?0.001). The post hoc analysis showed that both the conspicuity scores and contrast ratios of intracellular volume fraction (ICVF) derived from NODDI were higher than other image types. or the preparation of epilepsy surgery for TSC patients. ? ICVF derived from NODDI showed greater contrast than conventional MRI and DTI in detecting tubers, especially subtle epileptogenic ones. ? Diffusion parameters, especially ODI derived from NODDI, can support the identification of epileptogenicity.To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC).
This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test.
Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area undehted images and midtreatment DWI and ADC maps. ? RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.
? RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p less then 0.05) for predicting recurrence in breast cancer after NAC. ? Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps. ? RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.To assess the influence of patient characteristics, anatomical conditions, and technical factors on radiation exposure during prostatic arteries embolization (PAE) performed for benign prostatic hyperplasia.
Patient characteristics (age, body mass index (BMI)), anatomical conditions (number of prostatic arteries, anastomosis), and technical factors (use of cone beam computed tomography (CBCT), large display monitor (LDM), and magnification) were recorded as well as total air kerma (AK), dose area product (DAP), fluoroscopy time (FT), and number of acquisitions (NAcq). Associations between potential dose-influencing factors and AK using univariate analysis and a multiple linear regression model were assessed.
Forty-one consecutive men (68 ± 8 years, min-max 40-76) were included. LDM and CBCT decreased the use of small field of view with 13.9 and 3.8% respectively, both p &lt; 0.001. The use of a LDM significantly reduced AK (1006.6 ± 471.7 vs. 1412 ± 754.6 mGy, p = 0.02), DAP (119.4 ± 64.4 vs. 167.9 ± 99adiation exposure during PAE. ? Total air kerma is associated with patient's body mass index, fluoroscopy time, CBCT, and the number of acquisitions.The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease.
Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.
Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.